NLP TASK9 Attention原理

任务

基本的Attention原理。
HAN的原理（Hierarchical Attention Networks）。
利用Attention模型进行文本分类。

学习笔记

Attention原理

Attention是一种用于提升基于RNN（LSTM或GRU）的Encoder + Decoder模型的效果的的机制（Mechanism），一般称为Attention Mechanism。Attention Mechanism目前非常流行，广泛应用于机器翻译、语音识别、图像标注（Image Caption）等很多领域。Attention Mechanism可以帮助模型对输入的X每个部分赋予不同的权重，抽取出更加关键及重要的信息，使模型做出更加准确的判断，同时不会对模型的计算和存储带来更大的开销，这也是Attention Mechanism应用如此广泛的原因。Attention模块图解：

传统的Seq2Seq模型，想要解决的主要问题是，如何把机器翻译中，变长的输入X映射到一个变长输出Y的问题，但是该方法存在一个问题就是对输入序列X缺乏区分度。Attenction就可以用来解决该问题，通过计算$a_t$就将输入序列赋予不同重要性。

HAN的原理（Hierarchical Attention Networks）

Zichao Yang等人在论文《Hierarchical Attention Networks for Document Classification》提出了Hierarchical Attention用于文档分类。Hierarchical Attention构建了两个层次的Attention Mechanism，第一个层次是对句子中每个词的attention，即word attention；第二个层次是针对文档中每个句子的attention，即sentence attention。网络结构如图所示：

整个网络结构由四个部分组成：一个由双向RNN（GRU）构成的word sequence encoder，然后是一个关于词的word-level的attention layer；基于word attention layar之上，是一个由双向RNN构成的sentence encoder，最后的输出层是一个sentence-level的attention layer。

Attenction文本分类范例

#Attention模块代码 
#! -*- coding: utf-8 -*-

from keras import backend as K
from keras.engine.topology import Layer


class Position_Embedding(Layer):

    def __init__(self, size=None, mode='sum', **kwargs):
        self.size = size  # 必须为偶数
        self.mode = mode
        super(Position_Embedding, self).__init__(**kwargs)

    def call(self, x):
        if (self.size == None) or (self.mode == 'sum'):
            self.size = int(x.shape[-1])
        batch_size, seq_len = K.shape(x)[0], K.shape(x)[1]
        position_j = 1. / K.pow(10000., 2 * K.arange(self.size / 2, dtype='float32') / self.size)
        position_j = K.expand_dims(position_j, 0)
        position_i = K.cumsum(K.ones_like(x[:, :, 0]), 1) - 1  # K.arange不支持变长，只好用这种方法生成
        position_i = K.expand_dims(position_i, 2)
        position_ij = K.dot(position_i, position_j)
        position_ij = K.concatenate([K.cos(position_ij), K.sin(position_ij)], 2)
        if self.mode == 'sum':
            return position_ij + x
        elif self.mode == 'concat':
            return K.concatenate([position_ij, x], 2)

    def compute_output_shape(self, input_shape):
        if self.mode == 'sum':
            return input_shape
        elif self.mode == 'concat':
            return (input_shape[0], input_shape[1], input_shape[2] + self.size)


class Attention(Layer):

    def __init__(self, nb_head, size_per_head, **kwargs):
        self.nb_head = nb_head
        self.size_per_head = size_per_head
        self.output_dim = nb_head * size_per_head
        super(Attention, self).__init__(**kwargs)

    def build(self, input_shape):
        self.WQ = self.add_weight(name='WQ',
                                  shape=(input_shape[0][-1], self.output_dim),
                                  initializer='glorot_uniform',
                                  trainable=True)
        self.WK = self.add_weight(name='WK',
                                  shape=(input_shape[1][-1], self.output_dim),
                                  initializer='glorot_uniform',
                                  trainable=True)
        self.WV = self.add_weight(name='WV',
                                  shape=(input_shape[2][-1], self.output_dim),
                                  initializer='glorot_uniform',
                                  trainable=True)
        super(Attention, self).build(input_shape)

    def Mask(self, inputs, seq_len, mode='mul'):
        if seq_len == None:
            return inputs
        else:
            mask = K.one_hot(seq_len[:, 0], K.shape(inputs)[1])
            mask = 1 - K.cumsum(mask, 1)
            for _ in range(len(inputs.shape) - 2):
                mask = K.expand_dims(mask, 2)
            if mode == 'mul':
                return inputs * mask
            if mode == 'add':
                return inputs - (1 - mask) * 1e12

    def call(self, x):
        # 如果只传入Q_seq,K_seq,V_seq，那么就不做Mask
        # 如果同时传入Q_seq,K_seq,V_seq,Q_len,V_len，那么对多余部分做Mask
        if len(x) == 3:
            Q_seq, K_seq, V_seq = x
            Q_len, V_len = None, None
        elif len(x) == 5:
            Q_seq, K_seq, V_seq, Q_len, V_len = x
        # 对Q、K、V做线性变换
        Q_seq = K.dot(Q_seq, self.WQ)
        Q_seq = K.reshape(Q_seq, (-1, K.shape(Q_seq)[1], self.nb_head, self.size_per_head))
        Q_seq = K.permute_dimensions(Q_seq, (0, 2, 1, 3))
        K_seq = K.dot(K_seq, self.WK)
        K_seq = K.reshape(K_seq, (-1, K.shape(K_seq)[1], self.nb_head, self.size_per_head))
        K_seq = K.permute_dimensions(K_seq, (0, 2, 1, 3))
        V_seq = K.dot(V_seq, self.WV)
        V_seq = K.reshape(V_seq, (-1, K.shape(V_seq)[1], self.nb_head, self.size_per_head))
        V_seq = K.permute_dimensions(V_seq, (0, 2, 1, 3))
        # 计算内积，然后mask，然后softmax
        A = K.batch_dot(Q_seq, K_seq, axes=[3, 3]) / self.size_per_head ** 0.5
        A = K.permute_dimensions(A, (0, 3, 2, 1))
        A = self.Mask(A, V_len, 'add')
        A = K.permute_dimensions(A, (0, 3, 2, 1))
        A = K.softmax(A)
        # 输出并mask
        O_seq = K.batch_dot(A, V_seq, axes=[3, 2])
        O_seq = K.permute_dimensions(O_seq, (0, 2, 1, 3))
        O_seq = K.reshape(O_seq, (-1, K.shape(O_seq)[1], self.output_dim))
        O_seq = self.Mask(O_seq, Q_len, 'mul')
        return O_seq

    def compute_output_shape(self, input_shape):
        return (input_shape[0][0], input_shape[0][1], self.output_dim)

#带有Attention的文本分类网络
from __future__ import print_function

from keras.datasets import imdb
from keras.preprocessing import sequence

# from attention import Position_Embedding, Attention

max_features = 20000
maxlen = 80
batch_size = 32

print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(path="/home/admin-ygb/Desktop/learning/DataWhale_learning_nlp/data/imdb.npz",num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')

print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)

from keras.models import Model
from keras.layers import *

S_inputs = Input(shape=(None,), dtype='int32')
embeddings = Embedding(max_features, 128)(S_inputs)
embeddings = Position_Embedding()(embeddings)  # 增加Position_Embedding能轻微提高准确率
O_seq = Attention(8, 16)([embeddings, embeddings, embeddings])
O_seq = GlobalAveragePooling1D()(O_seq)
O_seq = Dropout(0.5)(O_seq)
outputs = Dense(1, activation='sigmoid')(O_seq)

model = Model(inputs=S_inputs, outputs=outputs)
print(model.summary())

# try using different optimizers and different optimizer configs
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

print('Train...')
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=5,
          validation_data=(x_test, y_test))

score, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print('Test score:', score)
print('Test accuracy:', acc)
#输出结果
Train...
Train on 25000 samples, validate on 25000 samples
Epoch 1/5
25000/25000 [==============================] - 163s 7ms/step - loss: 0.4904 - acc: 0.7382 - val_loss: 0.4334 - val_acc: 0.7972
Epoch 2/5
25000/25000 [==============================] - 138s 6ms/step - loss: 0.2798 - acc: 0.8832 - val_loss: 0.3503 - val_acc: 0.8431
Epoch 3/5
25000/25000 [==============================] - 151s 6ms/step - loss: 0.1814 - acc: 0.9302 - val_loss: 0.4032 - val_acc: 0.8327
Epoch 4/5
25000/25000 [==============================] - 143s 6ms/step - loss: 0.1004 - acc: 0.9644 - val_loss: 0.6076 - val_acc: 0.8122
Epoch 5/5
25000/25000 [==============================] - 142s 6ms/step - loss: 0.0435 - acc: 0.9854 - val_loss: 0.8017 - val_acc: 0.8092
25000/25000 [==============================] - 43s 2ms/step
Test score: 0.801709298927784
Test accuracy: 0.8092

参考资料

https://zhuanlan.zhihu.com/p/31547842
《Hierarchical Attention Networks for Document Classification》
https://github.com/foamliu/Self-Attention-Keras